Subterranean roadway deformation detection based on LiDAR scanning and fusion filtering

Abstract

Underground engineering is becoming increasingly important in modern urban construction and mine development. However, the shape of underground roadways may deform elastically or plastically due to geological conditions and accident loads, a phenomenon that cannot be ignored. Therefore, this paper proposes a roadway deformation detection method based on laser scanning. First, the working principle of the point cloud denoising and downsampling method is explained. To overcome the limitations of this method, the paper presents a point cloud denoising approach that combines statistical and median filtering. Additionally, it introduces a voxelised grid-downsampling technique based on density constraints and the centre of gravity. Next, the bidirectional projection method is used to determine the roadway’s central axis. Then, CloudCompare point cloud processing software is used to segment the point cloud, extract the roadway section, and fit a contour curve. Finally, the methods for extracting roadway deformation from processed point cloud data and for detecting and analysing it are introduced. Experiments on roadway deformation detection are conducted on an inspection robot experimental platform to verify the feasibility of the overall scheme. Experimental results indicate that the measurement error of light detection and ranging scanning for tunnel contour is less than 2 mm.

Keywords

Inspection robot / laser scanning / point cloud segmentation / fusion filtering / deformation detection

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